xref: /petsc/src/tao/bound/impls/bnk/bnls.c (revision 198282db2bfc4cc7307b0b13cb98d7a27b116ed7)

#include <../src/tao/bound/impls/bnk/bnk.h>
#include <petscksp.h>

/*
 Implements Newton's Method with a line search approach for
 solving bound constrained minimization problems.

 ------------------------------------------------------------

 x_0 = VecMedian(x_0)
 f_0, g_0 = TaoComputeObjectiveAndGradient(x_0)
 pg_0 = VecBoundGradientProjection(g_0)
 check convergence at pg_0
 trust = max_radius
 niter = 0

 while niter < max_it
    niter += 1
    H_k = TaoComputeHessian(x_k)
    if pc_type == BNK_PC_BFGS
      add correction to BFGS approx
      if scale_type == BNK_SCALE_AHESS
         D = VecMedian(1e-6, abs(diag(H_k)), 1e6)
         scale BFGS with VecReciprocal(D)
      end
    end

    if pc_type = BNK_PC_BFGS
      B_k = BFGS
    else
      B_k = VecMedian(1e-6, abs(diag(H_k)), 1e6)
      B_k = VecReciprocal(B_k)
    end
    w = x_k - VecMedian(x_k - 0.001*B_k*g_k)
    eps = min(eps, norm2(w))
    determine the active and inactive index sets such that
      L = {i : (x_k)_i <= l_i + eps && (g_k)_i > 0}
      U = {i : (x_k)_i >= u_i - eps && (g_k)_i < 0}
      F = {i : l_i = (x_k)_i = u_i}
      A = {L + U + F}
      I = {i : i not in A}

    generate the reduced system Hr_k dr_k = -gr_k for variables in I
    if p > 0
      Hr_k += p*I
    end
    if pc_type == BNK_PC_BFGS && scale_type == BNK_SCALE_PHESS
      D = VecMedian(1e-6, abs(diag(Hr_k)), 1e6)
      scale BFGS with VecReciprocal(D)
    end
    trust = max_radius
    solve Hr_k dr_k = -gr_k
    set d_k to (l - x) for variables in L, (u - x) for variables in U, and 0 for variables in F

    if dot(d_k, pg_k)) >= 0 || norm(d_k) == NaN || norm(d_k) == Inf
      dr_k = -BFGS*gr_k for variables in I
      if dot(d_k, pg_k)) >= 0 || norm(d_k) == NaN || norm(d_k) == Inf
        reset the BFGS preconditioner
        calculate scale delta and apply it to BFGS
        dr_k = -BFGS*gr_k for variables in I
        if dot(d_k, pg_k)) >= 0 || norm(d_k) == NaN || norm(d_k) == Inf
          dr_k = -gr_k for variables in I
        end
      end
    end

    x_{k+1}, f_{k+1}, g_{k+1}, ls_failed = TaoBNKPerformLineSearch()
    if ls_failed
      f_{k+1} = f_k
      x_{k+1} = x_k
      g_{k+1} = g_k
      pg_{k+1} = pg_k
      terminate
    else
      pg_{k+1} = VecBoundGradientProjection(g_{k+1})
      count the accepted step type (Newton, BFGS, scaled grad or grad)
    end

    check convergence at pg_{k+1}
 end
*/

static PetscErrorCode TaoSolve_BNLS(Tao tao)
{
  PetscErrorCode               ierr;
  TAO_BNK                      *bnk = (TAO_BNK *)tao->data;
  KSPConvergedReason           ksp_reason;
  TaoLineSearchConvergedReason ls_reason;

  PetscReal                    steplen = 1.0;
  PetscBool                    shift = PETSC_TRUE;
  PetscInt                     stepType;

  PetscFunctionBegin;
  /* Initialize the preconditioner, KSP solver and trust radius/line search */
  tao->reason = TAO_CONTINUE_ITERATING;
  ierr = TaoBNKInitialize(tao, BNK_INIT_CONSTANT);CHKERRQ(ierr);
  tao->trust = bnk->max_radius;
  if (tao->reason != TAO_CONTINUE_ITERATING) PetscFunctionReturn(0);

  /* Have not converged; continue with Newton method */
  while (tao->reason == TAO_CONTINUE_ITERATING) {
    ++tao->niter;
    tao->ksp_its=0;

    /* Compute the hessian and update the BFGS preconditioner at the new iterate*/
    ierr = TaoBNKComputeHessian(tao);CHKERRQ(ierr);

    /* Use the common BNK kernel to compute the safeguarded Newton step (for inactive variables only) */
    ierr = TaoBNKComputeStep(tao, shift, &ksp_reason);CHKERRQ(ierr);
    ierr = TaoBNKSafeguardStep(tao, ksp_reason, &stepType);CHKERRQ(ierr);

    /* Store current solution before it changes */
    bnk->fold = bnk->f;
    ierr = VecCopy(tao->solution, bnk->Xold);CHKERRQ(ierr);
    ierr = VecCopy(tao->gradient, bnk->Gold);CHKERRQ(ierr);
    ierr = VecCopy(bnk->unprojected_gradient, bnk->unprojected_gradient_old);CHKERRQ(ierr);

    /* Trigger the line search */
    ierr = TaoBNKPerformLineSearch(tao, stepType, &steplen, &ls_reason);CHKERRQ(ierr);

    if (ls_reason != TAOLINESEARCH_SUCCESS && ls_reason != TAOLINESEARCH_SUCCESS_USER) {
      /* Failed to find an improving point */
      bnk->f = bnk->fold;
      ierr = VecCopy(bnk->Xold, tao->solution);CHKERRQ(ierr);
      ierr = VecCopy(bnk->Gold, tao->gradient);CHKERRQ(ierr);
      ierr = VecCopy(bnk->unprojected_gradient_old, bnk->unprojected_gradient);CHKERRQ(ierr);
      steplen = 0.0;
      tao->reason = TAO_DIVERGED_LS_FAILURE;
    } else {
      /* compute the projected gradient */
      ierr = VecBoundGradientProjection(bnk->unprojected_gradient,tao->solution,tao->XL,tao->XU,tao->gradient);CHKERRQ(ierr);
      /* count the accepted step type */
      ierr = TaoBNKAddStepCounts(tao, stepType);CHKERRQ(ierr);
    }

    /*  Check for termination */
    ierr = TaoGradientNorm(tao, tao->gradient, NORM_2, &bnk->gnorm);CHKERRQ(ierr);
    if (PetscIsInfOrNanReal(bnk->f) || PetscIsInfOrNanReal(bnk->gnorm)) SETERRQ(PETSC_COMM_SELF, 1, "User provided compute function generated Not-a-Number");
    ierr = TaoLogConvergenceHistory(tao, bnk->f, bnk->gnorm, 0.0, tao->ksp_its);CHKERRQ(ierr);
    ierr = TaoMonitor(tao, tao->niter, bnk->f, bnk->gnorm, 0.0, steplen);CHKERRQ(ierr);
    ierr = (*tao->ops->convergencetest)(tao, tao->cnvP);CHKERRQ(ierr);
  }
  PetscFunctionReturn(0);
}

/*------------------------------------------------------------*/

PETSC_EXTERN PetscErrorCode TaoCreate_BNLS(Tao tao)
{
  TAO_BNK        *bnk;
  PetscErrorCode ierr;

  PetscFunctionBegin;
  ierr = TaoCreate_BNK(tao);CHKERRQ(ierr);
  tao->ops->solve = TaoSolve_BNLS;

  bnk = (TAO_BNK *)tao->data;
  bnk->init_type = BNK_INIT_CONSTANT;
  bnk->update_type = BNK_UPDATE_STEP; /* trust region updates based on line search step length */
  PetscFunctionReturn(0);
}